4 citations · 9 across the 6 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2024
Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs
Anika Hannemann, Jan Ewald, Leo Seeger +1
Machine learning on large-scale genomic or transcriptomic data is important for many novel health applications. For example, precision medicine tailors medical treatments to patien…
cs.LG2023★ 4 cited
A Privacy-Preserving Federated Learning Approach for Kernel methods
Anika Hannemann, Ali Burak Ünal, Arjhun Swaminathan +2
It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, i…